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While uncertainty has always been a feature of the healthcare environment, its pace and scope are rapidly increasing, fueled by myriad factors such as technological advancements, the threat and frequency of disruptive events, global economic developments, and increasing complexity. Contemporary healthcare organizations thus persistently face what is known as “deep uncertainty,” which obscures their ability to predict outcomes of strategic action and decision-making, presenting them with novel challenges and threatening their survival. Persistent, deep uncertainty challenges us to revisit and reconsider how we think about uncertainty and the strategic actions needed by organizations to thrive under these circumstances. Simply put, how can healthcare organizations thrive in the face of deeply uncertain environments? We argue that healthcare organizations need to employ both adaptive and creative strategic approaches in order to effectively meet patients' needs and capture value in the long-term future. The chapter concludes by offering two ways organizations can build the dynamic capabilities needed to employ such approaches.

An ecologically sustainable future calls for fruitful dialogues between spirituality, modern science and policymaking at large. What could be that connects them all? We found out that ideas about holism exist across time, space, culture and thinkers – ranging from mathematics, philosophy, sociology, medicine, education, religion and quantum physics to finding its roots in ancient Indian Vedic tradition and later usage in Greek and Roman cultures.

This chapter takes a look at the history and intricacies of two seemingly distinct but interconnected fields – spirituality and modern science, particularly quantum science – with an aim to uncover what these fields can teach us about the idea of holism. This chapter, therefore, highlights one of the most fundamental and profound spiritual principles of the unity and interconnectedness of the entire universe – encapsulated in the concept of holism – and its practical applications in approaching sustainable development. We hope to ignite further research on this topic.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

Higher education institutions (HEIs) are increasingly recognizing that their unique functions and expertise in research, education, and community leadership make them essential societal partners for helping achieve the Sustainable Development Goals (SDGs). However, the sector is not reaching its full potential, or acting fast enough, given the dire state of global progress on the SDGs. There has been a growing recognition that higher education’s (HE) ability to scale up action on the SDGs is hampered by a range of systemic and structural barriers within institutions, the HE sector, and the local and global contexts more broadly. However, many of these barriers and the potential solutions for overcoming them have been known for years, and a key challenge HE now faces is how to put these changes into practice. In this chapter, we build on insights from the work of the Sustainable Development Solutions Network (SDSN) and others on system transformations for sustainable development, as well as our own work on HE engagement with the SDGs, to propose several ‘meta’ reasons that might be hampering efforts to scale up HE action on the SDGs, as well as some suggested approaches for addressing them. These approaches include treating HE as a system, defining better the outcomes we are aiming for, employing adaptive leadership approaches, and investing in genuine partnerships. While a detailed treatment of these approaches is beyond the scope of this chapter, we hope to encourage the HE community to look at this old problem in new ways.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

The aim of this chapter is to stimulate debate and critical thinking about the relevance and effectiveness – especially for ‘non-core’ areas – of prevailing methodological approaches to entrepreneurial ecosystems (EE) research and policy development and to argue for the exploration of critical realism (CR) as a potential framework for researchers, practitioners and policymakers. I argue that prevailing theoretical and methodological frameworks are insufficient to enable researchers to adequately understand the dynamics of diverse EE or for policymakers to act appropriately on research findings. I posit CR as an alternative framework, providing a ‘lens’ through which researchers, policymakers and other ecosystem stakeholders can collaboratively explore the role of context and its interplay with agency. CR addresses the disadvantages associated with deterministic models of entrepreneurship and ‘Valley envy’ policy prescriptions, thus generating more meaningful insights for policy and practice, especially in relation to ‘non-core’ areas.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

In this chapter, the author explains the three components of Trilogy of Taoist Leadership – responsible business, responsible management and responsible leadership. The concepts of business, management and leadership are defined from multiple angles, including an etymological perspective. The historical origins and evolution of these three areas are explored to provide a comprehensive understanding. The chapter concludes with a discussion of the interconnections among the trilogy's components and illustrates how the trilogy can contribute to the development of sustainable organizations.